Holter recordings are in common usage recorded on magnetic tape in view of the volume of information to memorize, i.e., to record or store in a memory. More recently, these recordings are, increasingly stored in a static semi-conductor memory device, a so-called "Holter memory". The utilization of this technology is however limited by the lesser capacity of static memories as compared to magnetic tapes, which obliges, at the moment of the acquisition of data and before recording the acquired data in memory, to use a compression algorithm to economize use of the available storage capacity in the Holter memory.
Thus, if one wants to record during a 24 hour period the ECG signals collected on two channels (leads) and if the ECG signal on each channel is sampled at 100 Hz with a resolution of 10 .mu.V and a dynamic range of 10 mV, the flow of information on each ECG channel is 1000 bits/second (100 words of 10 bits each). If, for example, one has static memories allowing a capacity of Holter memory of 10 Megabytes (MB), on which one reserves 1 MB for files of analysis results, histogram, etc., there remains 4.5 MB available to memorize the 24 hours of signals of each of channel. This constraint of size imposes a serious limitation on the flow of data to memorize 52.08 bytes/second, corresponding to 422.4 bits/second; it is therefore necessary to compress data by a factor of at least 1000/422.4=2.36.
For a signal having more detailed information, and where higher resolution is appropriate, the processor can sample (digitize) the ECG signals at a higher sampling frequency, 200 Hz, for example. All other things being equal in such a case, the rate of compression has then to increase to 4.72.
Heretofore, a number of processes have been proposed to compress physiological data such as Holter information. The particular problems posed by the ambulatory recorded ECG are its irregularity and the presence of many artifacts. The ECG signal typically is constituted by the cardiac origin signal, which is almost periodic (the PQRST complex), accompanied by signals generated by muscles, by mechanical perturbations of the electrode--skin interface, and by electrical perturbations such as electromagnetic interference captured by the cables connecting electrodes to the recorder.
Classic algorithms appear to be relatively effective in the particular case of regular ECG signals. However, concerning signals that are affected with significant parasitic signals, the classic algorithms are inadequate to eliminate the parasites.
On the other hand, it is important that the data compression/decompression sequence does not introduce artifacts that are too visible. Such artifacts occur, for example, in the case of the known compressions using approximations by parabolic or right segment arcs. Such artifacts introduce to the display screen waveform "breakages" that are very visible to the eye. These breakages are susceptible to impair the interpretation of the ECG waveform trace by the therapist. In this regard, a reconstructed displayed ECG trace may have an appearance that is similar to a different "normal" tracing, and the therapist may incorrectly interpret the reconstructed displayed trace with its breakages as the normal ECG without realizing the error.
In addition, in the case of an ECG signal, it is important that one may observe the variability of the QRS complex, which can be very significant for the diagnosis, and therefore one must be able to preserve a certain number of micro-variations.
This constraint renders, in practice, an inefficient predictive compression algorithm, based on the repetition of an average signal (the PQRST complex), because the multitude of forms of different PQRST complexes renders their operation very difficult and their performance degrades rapidly. The compression algorithm not only has to function in the ideal case, that is to say for a regular ECG and without parasitic signals, but it also must be able to support very irregular ECGs (frequent abnormal complexes, of a form very variable, for example, with stable periods), some of which contain artifacts.
Another typical difficulty in recording Holter data is the fact that machines have to be able to function autonomously on batteries for 24 and sometimes 48 hours. Now, the utilization of a complex algorithm necessitates a sufficiently powerful processor functioning more or less continuously. This implies a large energy consumption and a limitation of the duration of recording, to minimize the weight and the size of the machine.